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Wiley Interdisciplinary Reviews: Computational Statistics, 2009
AbstractRidge regression is a popular parameter estimation method used to address the collinearity problem frequently arising in multiple linear regression. The formulation of the ridge methodology is reviewed and properties of the ridge estimates capsulated.
Gary C Mcdonald
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AbstractRidge regression is a popular parameter estimation method used to address the collinearity problem frequently arising in multiple linear regression. The formulation of the ridge methodology is reviewed and properties of the ridge estimates capsulated.
Gary C Mcdonald
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A Poisson ridge regression estimator
Economic Modelling, 2011 The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method.
Månsson, Kristofer, Shukur, Ghazi
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International Journal of Fuzzy Systems, 2019
Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model.
Seung Hoe Choi +2 more
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Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model.
Seung Hoe Choi +2 more
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Ridge regression:some simulations
Communications in Statistics, 1975An algorithm is given for selacting the biasing paramatar, k, in RIDGE regrassion. By means of simulaction it is shown that the algorithm has the following properties: (i) it produces an aberaged squared error for the regrassion coafficiants that is les than least squares, (ii) the distribuction of squared arrots for the regression coafficiants has a ...
Arthur E Hoerl
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Reduced Rank Kernel Ridge Regression
Neural Processing Letters, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cawley, Gavin C., Talbot, Nicola L. C.
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Journal of Chemometrics, 1997
Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least squares (PLS) on the basis of two data sets.
Evelyne Vigneau
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Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least squares (PLS) on the basis of two data sets.
Evelyne Vigneau
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Detection of influential observations in ridge regression and modified ridge regression
Model Assisted Statistics and Applications, 2012The detection of influential observations is important because of their unduly large influence on the regression analysis results. Numerous diagnostics on identifying these observations are developed in the regression analysis. Pena's statistic is one of the proposed diagnostics. In this study, Pena's approach is formulated to ridge regression (RR) and
Semra Türkan, Öniz Toktamis
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Modified Ridge Regression Estimators
Communications in Statistics - Theory and Methods, 2013 Ridge Regression is a variant of ordinary multiple linear regression whose goal is to circumvent the problem of predictors collinearity. It gives-up the Ordinary Least Squares (OLS) estimator as a method for estimating the parameters of the multiple ...
Kristofer Mansson, Ghazi Shukur
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